Dialogue systems, whether they are trained in an end-to-end [1]}  fashion or in a modular way [2]}, require large amounts of annotated data from both human-human and human-machine interactions, using natural or unnatural or constrained settings [3]}.
Even though there is an increasing interest in building systems using publicly available datasets and improving benchmarks for general-domain dialogue systems such as in bAbl tasks [4]}, the distribution of datasets in the biomedical domain is quite limited. For example, the methodical review of  [5]} shows that NLP related biomedical research involves a lot of private datasets which are rarely shared or replicated due to patient privacy concerns. This situation is even more difficult for languages other than English which can be subject to different regulations.
